Generating a recommendation to add a member to a receptivity cohort

ABSTRACT

A processor-implemented method, system, and/or computer program product generate a recommendation to add a member to a receptivity cohort. A receptivity cohort is made up of members who share a conduct attribute, which is a facial expression, body language, and/or social interaction of a person. The conduct attribute has been predetermined to be an indicator of a level of receptiveness to a proposed future change in a set of circumstances. Biometric sensor data, which describe the facial expression, body language and/or social interaction for a candidate member, are retrieved from a set of biometric sensors. The retrieved biometric sensor data for the candidate member is compared to the conduct attribute of members of the receptivity cohort. In response to the biometric sensor data for the candidate member matching the conduct attribute of members of the receptivity cohort, a recommendation is generated to add the candidate member to the receptivity cohort.

The present application is a continuation of U.S. patent applicationSer. No. 12/335,731, filed on Dec. 16, 2008, and titled “GeneratingReceptivity Scores For Cohorts,” which is incorporated herein byreference.

BACKGROUND

The present invention relates generally to an improved data processingsystem and in particular to a method and apparatus for generatingcohorts. More particularly, the present invention is directed to acomputer implemented method, apparatus, and computer usable program codefor processing cohort data to generate receptivity scores.

A cohort is a group of members selected based upon a commonality of oneor more attributes. For example, one attribute may be a level ofeducation attained by workers. Thus, a cohort of workers in an officebuilding may include members who have graduated from an institution ofhigher education. In addition, the cohort of workers may include one ormore sub-cohorts that may be identified based upon additional attributessuch as, for example, a type of degree attained, a number of years theworker took to graduate, or any other conceivable attribute. In thisexample, such a cohort may be used by an employer to correlate aworker's level of education with job performance, intelligence, and/orany number of variables. The effectiveness of cohort studies dependsupon a number of different factors, such as the length of time that themembers are observed, and the ability to identify and capture relevantdata for collection. For example, the information that is needed orwanted to identify attributes of potential members of a cohort may bevoluminous, dynamically changing, unavailable, difficult to collect,and/or unknown to the members of the cohort and/or the user selectingmembers of the cohort. Moreover, it may be difficult, time consuming, orimpractical to access all the information necessary to accuratelygenerate cohorts. Thus, unique cohorts may be sub-optimal becauseindividuals lack the skill, time, knowledge, and/or expertise needed togather cohort attribute information from available sources.

SUMMARY

A processor-implemented method, system, and/or computer program productgenerate a recommendation to add a member to a receptivity cohort. Areceptivity cohort is made up of members who share a conduct attribute,which is a facial expression, body language, and/or social interactionof a person. The conduct attribute has been predetermined to be anindicator of a level of receptiveness to a proposed future change in aset of circumstances. Biometric sensor data, which describe the facialexpression, body language and/or social interaction for a candidatemember, are retrieved from a set of biometric sensors. The retrievedbiometric sensor data for the candidate member is compared to theconduct attribute of members of the receptivity cohort. In response tothe biometric sensor data for the candidate member matching the conductattribute of members of the receptivity cohort, a recommendation isgenerated to add the candidate member to the receptivity cohort.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 is a block diagram of a receptivity analysis system forgenerating receptivity cohorts in accordance with an illustrativeembodiment;

FIG. 4 is a block diagram of a set of multimodal sensors in accordancewith an illustrative embodiment;

FIG. 5 is a diagram of a set of cohorts used to generate a receptivityscore in accordance with an illustrative embodiment;

FIG. 6 is a block diagram of description data for an individual inaccordance with an illustrative embodiment;

FIG. 7 is a block diagram of a value assigned to a conduct attribute inaccordance with an illustrative embodiment; and

FIG. 8 is a flowchart of a process for generating a receptivity score inaccordance with an illustrative embodiment.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including, but not limited to, wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

With reference now to the figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments may be implemented. It shouldbe appreciated that FIGS. 1-2 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made.

FIG. 1 depicts a pictorial representation of a network of dataprocessing systems in which illustrative embodiments may be implemented.Network data processing system 100 is a network of computers in whichthe illustrative embodiments may be implemented. Network data processingsystem 100 contains network 102, which is the medium used to providecommunications links between various devices and computers connectedtogether within network data processing system 100. Network 102 mayinclude connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage unit 108. In addition, clients 110, 112, and 114connect to network 102. Clients 110, 112, and 114 may be, for example,personal computers or network computers. In the depicted example, server104 provides data, such as boot files, operating system images, andapplications to clients 110, 112, and 114. Clients 110, 112, and 114 areclients to server 104 in this example. Network data processing system100 may include additional servers, clients, and other devices notshown.

Program code located in network data processing system 100 may be storedon a computer recordable storage medium and downloaded to a dataprocessing system or other device for use. For example, program code maybe stored on a computer recordable storage medium on server 104 anddownloaded to client 110 over network 102 for use on client 110.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation for thedifferent illustrative embodiments.

With reference now to FIG. 2, a block diagram of a data processingsystem is shown in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as, withoutlimitation, server 104 or client 110 in FIG. 1, in which computer usableprogram code or instructions implementing the processes may be locatedfor the illustrative embodiments. In this illustrative example, dataprocessing system 200 includes communications fabric 202, which providescommunications between processor unit 204, memory 206, persistentstorage 208, communications unit 210, input/output (I/O) unit 212, anddisplay 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206 and persistent storage 208 are examples of storage devices. Astorage device is any piece of hardware that is capable of storinginformation either on a temporary basis and/or a permanent basis. Memory206, in these examples, may be, for example, a random access memory orany other suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. For example, persistent storage 208 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used bypersistent storage 208 also may be removable. For example, a removablehard drive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard and mouse. Further, input/output unit 212 may sendoutput to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer readable media 218 form computerprogram product 220 in these examples. In one example, computer readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 218 is also referred to as computerrecordable storage media. In some instances, computer recordable media218 may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. The computerreadable media also may take the form of non-tangible media, such ascommunications links or wireless transmissions containing the programcode.

In some illustrative embodiments, program code 216 may be downloadedover a network to persistent storage 208 from another device or dataprocessing system for use within data processing system 200. Forinstance, program code stored in a computer readable storage medium in aserver data processing system may be downloaded over a network from theserver to data processing system 200. The data processing systemproviding program code 216 may be a server computer, a client computer,or some other device capable of storing and transmitting program code216.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown.

As one example, a storage device in data processing system 200 is anyhardware apparatus that may store data. Memory 206, persistent storage208, and computer readable media 218 are examples of storage devices ina tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter. Amemory may be, for example, memory 206 or a cache such as found in aninterface and memory controller hub that may be present incommunications fabric 202.

The illustrative embodiments recognize that the ability to quickly andaccurately perform an assessment of a person's conduct to identify theperson's receptiveness to a proposed future change, job offer, offer tosell a product, offer to purchase a product, or other events thatrequire a person cooperation or agreement in different situations andcircumstances may be valuable to business planning, hiring workers,health, safety, marketing, mergers, transportation, retail, and variousother industries. Thus, according to one embodiment of the presentinvention, a computer implemented method, apparatus, and computerprogram product for analyzing sensory input data and cohort dataassociated with a set of individuals to generate receptivity cohorts isprovided.

According to one embodiment of the present invention, digital sensordata associated with a set of individuals is retrieved in response toreceiving an identification of a proposed future change in a current setof circumstances associated with the set of individuals. As used herein,the term “set” refers to one or more. Thus, the set of individuals maybe a single individual, as well as two or more individuals.

The digital sensor data comprises events metadata describing a set ofevents associated with the set of individuals. The set of eventscomprises at least one of body language, facial expressions,vocalizations, and social interactions of the set of individuals. Asused herein, the term “at least one of”, when used with a list of items,means that different combinations of one or more of the items may beused and only one of each item in the list may be needed. For example,“at least one of item A, item B, and item C” may include, for example,without limitation, item A alone, item B alone, item C alone, acombination of item A and item B, a combination of item B and item C, acombination of item A and item C, or a combination that includes item A,item B, and item C.

An analysis server selects a set of receptivity analysis models based onthe proposed future event and the set of events. Each analysis model inthe set of receptivity analysis models analyzes the set of events toidentify a set of conduct attributes indicating receptiveness of eachindividual in the set of individuals to the proposed future change. Theevents metadata describing the set of events is analyzed in the selectedset of receptivity analysis models to form a receptivity cohort. Thereceptivity cohort comprises a set of conduct attributes indicatingreceptiveness of each individual in the set of individuals to theproposed future change.

A cohort is a group of people or objects. Members of a cohort share acommon attribute or experience in common. A cohort may be a member of alarger cohort. Likewise, a cohort may include members that arethemselves cohorts, also referred to as sub-cohorts. In other words, afirst cohort may include a group of members that forms a sub-cohort.That sub-cohort may also include a group of members that forms asub-sub-cohort of the first cohort, and so on. A cohort may be a nullset with no members, a set with a single member, as well as a set ofmembers with two or more members.

According to one embodiment of the present invention, a computerimplemented method, apparatus, and computer program product forgenerating receptivity scores for cohorts is provided. A receptivitycohort is identified. The receptivity cohort includes a set of membersand conduct attributes for the set of members. Each conduct attribute inthe set of conduct attributes describes at least one of a facialexpression, vocalization, body language, and social interactions of amember in the set of members. Each conduct attribute is an indicator ofreceptiveness to a proposed future change in a set of circumstancesassociated with the set of members. Events metadata is received. Theevents metadata describes the set of circumstances associated with theset of members. The set of conduct attributes and the events metadata isanalyzed to generate a receptivity score for the receptivity cohort. Thereceptivity score indicates a level of receptiveness of the set ofmembers to the proposed future change in the set of circumstances. Theset of members of the receptivity cohort are identified as receptive tothe proposed future change based on the result of a comparison of thereceptivity score to a threshold score.

FIG. 3 is a block diagram of a receptivity analysis system forgenerating receptivity cohorts in accordance with an illustrativeembodiment. Analysis server 300 is a server for analyzing sensor inputassociated with one or more individuals. Analysis server 300 may beimplemented, without limitation, on a software server located on ahardware computing device, such as, but not limited to, a main frame, aserver, a personal computer, laptop, personal digital assistant (PDA),or any other computing device depicted in FIGS. 1 and 2.

Analysis server 300 receives an identification of a proposed futurechange 301 in a current set of circumstances associated with a set ofindividuals. The proposed future change 301 is an event or action thathas not yet occurred but may occur in the future. Proposed future change301 may require the agreement or cooperation of at least one individualin set of individuals if the change is going to occur. For example,proposed future change 301 may be, without limitation, a job offer thatrequires the worker to move to another city, another state, or adifferent country. In another non-limiting example, proposed futurechange 301 may be an offer to buy the individual's house. If theindividual accepts the offer, the individual agrees to move out andchange residences. In yet another example, proposed future change 301may be a marketing offer proposing the purchase of goods or services ata particular prices. Proposed future change 301 may offer to sell acustomer a larger size box of laundry detergent for a discount price ifthe customer chooses to buy the larger size over the smaller size of thesame brand of laundry detergent.

Proposed future change 301 may also include, without limitation, aproposed future change in work location requiring that a worker commuteacross a greater distance, a proposed offer to sell goods or services toa customer at a given price, an offer to purchase goods or services froma person, a proposed request that a person leave a particular location,or a request that a person stop performing a given action.

In response to receiving proposed future change 301, analysis server 300retrieves multimodal sensor data 302 for the set of individuals from aset of multimodal sensors. A multimodal sensor may be a camera, an audiodevice, a biometric sensor, a chemical sensor, or a sensor and actuator,such as set of multimodal sensors 400 in FIG. 4 below. Multimodal sensordata 302 is data that describes the set of individuals. In other words,multimodal sensors record readings for the set of individuals using avariety of sensor devices to form multimodal sensor data 302. Forexample, multimodal sensor data that is generated by a camera includesimages of at least one individual in the set of individuals. Multimodalsensor data that is generated by a microphone includes audio data ofsounds made by at least one individual in the set of individuals. Thus,multimodal sensor data 302 may include, without limitation, sensor inputin the form of audio data, images from a camera, biometric data, signalsfrom sensors and actuators, and/or olfactory patterns from an artificialnose or other chemical sensor.

Sensor analysis engine 304 is software architecture for analyzingmultimodal sensor data 302 to generate digital sensor data 306. Analogto digital conversion 308 is a software component that converts anymultimodal sensor data that is in an analog format into a digitalformat. Analog to digital conversion 308 may be implemented using anyknown or available analog to digital converter (ADC). Sensor analysisengine 304 processes and parses the sensor data in the digital format toidentify attributes of the set of individuals. Metadata generator 310 isa software component for generating metadata describing the identifiedattributes of the set of individuals.

Sensor analysis engine 304 may include a variety of software tools forprocessing and analyzing the different types of sensor data inmultimodal sensor data 302. Sensor analysis engine 304 may include,without limitation, olfactory analytics for analyzing olfactory sensorydata received from chemical sensors, video analytics for analyzingimages received from cameras, audio analytics for analyzing audio datareceived from audio sensors, biometric data analytics for analyzingbiometric sensor data from biometric sensors, and sensor and actuatorsignal analytics for analyzing sensor input data from sensors andactuators.

Sensor analysis engine 304 may be implemented using a variety of digitalsensor analysis technologies, such as, without limitation, video imageanalysis technology, facial recognition technology, license platerecognition technology, and sound analysis technology. In oneembodiment, sensor analysis engine 304 is implemented using, withoutlimitation, IBM® smart surveillance system (S3) software.

Sensor analysis engine 304 utilizes computer vision and patternrecognition technologies, as well as video analytics to analyze videoimages captured by one or more situated cameras, microphones, or othermultimodal sensors. The analysis of multimodal sensor data 302 generatesevents metadata 312 describing set of events 320 of interest in theenvironment. Set of events 320 is events performed by the set ofindividuals or occurring in proximity to the set of individuals. Set ofevents 320 includes the conduct of set of individuals and thecircumstances surrounding the set of individuals when the conductoccurs.

Sensor analysis engine 304 includes video analytics software foranalyzing video images and audio files generated by the multimodalsensors. The video analytics may include, without limitation, behavioranalysis, license plate recognition technology, face recognitiontechnology, badge reader, and radar analytics technology. Behavioranalysis technology tracks moving objects and classifies the objectsinto a number of predefined categories by analyzing metadata describingimages captured by the cameras. As used herein, an object may be ahuman, an object, a container, a cart, a bicycle, a motorcycle, a car, alocation, or an animal, such as, without limitation, a dog. Licenseplate recognition technology may be utilized to analyze images capturedby cameras deployed at the entrance to a facility, in a parking lot, onthe side of a roadway or freeway, or at an intersection. License platerecognition technology catalogs a license plate of each vehicle movingwithin a range of two or more video cameras associated with sensoranalysis engine 304. For example, license plate recognition technologymay be utilized to identify a license plate number on license plate.

Face recognition technology is software for identifying a human based onan analysis of one or more images of the human's face. Face recognitiontechnology may be utilized to analyze images of objects captured bycameras deployed at entryways, or any other location, to capture andrecognize faces. Badge reader technology may be employed to read badges.The information associated with an object obtained from the badges isused in addition to video data associated with the object to identify anobject and/or a direction, velocity, and/or acceleration of the object.

The data gathered from behavior analysis, license plate recognitiontechnology, facial recognition technology, badge reader, radaranalytics, and any other video/audio data received from a camera orother video/audio capture device is received by sensor analysis engine304 for processing into events metadata 312 describing events,identification attributes 314 of one or more objects in a given area,and/or circumstances associated with the set of individuals. The eventsfrom all these technologies are cross indexed into a common repositoryor a multi-mode event database allowing for correlation across multipleaudio/video capture devices and event types. In such a repository, asimple time range query across the modalities will extract license plateinformation, vehicle appearance information, badge information, objectlocation information, object position information, vehicle make, model,year and/or paint, and face appearance information. This permits sensoranalysis engine 304 to easily correlate these attributes.

Digital sensor data 306 comprises events metadata 312 describing set ofevents 320 associated with an individual in the set of individuals. Anevent is an action or event that is performed by the individual or inproximity to the individual. An event may be the individual making asound, walking, eating, making a facial expression, a change in theindividual's posture, spoken words, the individual throwing an object,talking to someone, carrying a toddler, holding hands with someone,picking up an object, standing still, or any other movement, conduct, orevent.

Digital sensor data 306 may also optionally include identificationattributes 314. An attribute is a characteristic, feature, or propertyof an object. An identification attribute is an attribute that may beused to identify a person. In a non-limiting example, identificationattribute may include a person's name, address, eye color, age, voicepattern, color of their jacket, size of their shoes, retinal pattern,iris pattern, fingerprint, thumbprint, palm print, facial recognitiondata, badge reader data, smart card data, scent recognition data,license plate number, and so forth. Attributes of a thing may includethe name of the thing, the value of the thing, whether the thing ismoving or stationary, the size, height, volume, weight, color, orlocation of the thing, and any other property or characteristic of thething.

Cohort generation engine 316 receives digital sensor data 306 fromsensor analysis engine 304. Cohort generation engine 316 may requestdigital sensor data 306 from sensor analysis engine 304 or retrievedigital sensor data 306 from data storage device 318. In anotherembodiment, sensor analysis engine 304 automatically sends digitalsensor data 306 to cohort generation engine 316 in real time as digitalsensor data 306 is generated. In yet another embodiment, sensor analysisengine 304 sends digital sensor data 306 to cohort generation engine 316upon the occurrence of a predetermined event. A predetermined event maybe, but is not limited to, a given time, completion of processingmultimodal sensor data 302, occurrence of a timeout event, a userrequest for generation of set of cohorts based on digital sensor data306, or any other predetermined event. The illustrative embodiments mayutilize digital sensor data 306 in real time as digital sensor data 306is generated. The illustrative embodiments may also utilize digitalsensor data 306 that is pre-generated or stored in data storage device318 until the digital sensor data is retrieved at some later time.

Data storage device 318 may be a local data storage located on the samecomputing device as cohort generation engine 316. In another embodiment,data storage device 318 is located on a remote data storage device thatis accessed through a network connection. In yet another embodiment,data storage device 318 may be implemented using two or more datastorage devices that may be either local or remote data storage devices.

Cohort generation engine 316 retrieves any of description data 322 forthe set of individuals that is available. Description data 322 mayinclude identification information identifying the individual, pasthistory information for the individual, and/or current statusinformation for the individual. Information identifying the individualmay be a person's name, address, age, birth date, social securitynumber, worker identification number, or any other identificationinformation. Past history information is any information describing pastevents associated with the individual. Past history information mayinclude medical history, work history/employment history, previouspurchases, discounts and sale items purchased, customer rewardmemberships and utilization of rewards, social security records,judicial record, consumer history, educational history, previousresidences, prior owned property, repair history of property owned bythe individual, or any other past history information. For example,education history may include, without limitation, schools attended,degrees obtained, grades earned, and so forth. Medical history mayinclude previous medical conditions, previous medications prescribed tothe individual, previous physicians that treated the individual, medicalprocedures/surgeries performed on the individual, and any other pastmedical information.

Current status information is any information describing a currentstatus of the individual. Current status information may include, forexample and without limitation, scheduled events, an identification ofitems in a customer's shopping cart, current medical condition, currentprescribed medications, a customer's current credit score, currentstatus of the individual's driver's license, such as whether a licenseis valid or suspended, current residence, marital status, and any othercurrent status information.

Cohort generation engine 316 optionally retrieves demographicinformation 324 from data storage device 318. Demographic information324 describes demographic data for the individual's demographic group.Demographic information 324 may be obtained from any source thatcompiles and distributes demographic information. For example, if theset of individuals includes a single mother of two offspring, that has abachelor's degree, and lives in Boulder, Colo., and demographic data forother single, educated, parents that have been presented with similarproposed future changes may be useful in determining whether this singleparent will be receptive to similar proposed future changes.

In another embodiment, cohort generation engine 316 receives manualinput 326 that provides manual input describing the individual and/ormanual input defining the analysis of events metadata 312 and/oridentification attributes 314 for the set of individuals.

In another embodiment, if description data 322 and/or demographicinformation 324 is not available, data mining and query search 329searches set of sources 331 to identify additional description data forthe individual and demographic information for each individual'sdemographic group. Set of sources 331 may include online sources, aswell as offline sources. Online sources may be, without limitation, webpages, blogs, wikis, newsgroups, social networking sites, forums, onlinedatabases, and any other information available on the Internet. Off-linesources may include, without limitation, relational databases, datastorage devices, or any other off-line source of information.

Cohort generation engine 316 selects a set of receptivity analysismodels for use in processing set of events 320, identificationattributes 314, description data 322, demographic data 324, and/ormanual input 326. Cohort generation engine 316 selects the receptivityanalysis models based on proposed future change 301, the type of eventsmetadata, the events in set of events 320, available demographicinformation 324, and the available description data to form set ofreceptivity analysis models 325. In this example, set of receptivityanalysis models 325 may include, without limitation, deportment analysismodel 326, comportment analysis model 328, social interactions analysismodel 330, and marketing analysis 332.

Deportment refers to the way a person behaves toward other people,demeanor, conduct, behavior, manners, social deportment, citizenship,swashbuckling, correctitude, properness, propriety, improperness,impropriety, and manner. Swashbuckling refers to flamboyant, reckless,or boastful behavior. Deportment analysis model 326 analyzes set ofevents 320 to identify conduct attributes 334. A conduct attributedescribes a facial expression, a person's vocalization, body language,social interactions, and any other motions or movements of an individualthat is determined to be an indicator of receptiveness of theindividual. A conduct attribute may be used to identify an emotionalstate, demeanor, conduct, manner, social deportment, propriety,impropriety, and flamboyant actions of the set of individuals. Anemotional state of an individual comprises at least one of fear, joy,happiness, anger, jealousy, embarrassment, depression, and anunemotional state, such as when a person is calm or the person's face isexpressionless.

Deportment analysis model 326 may utilize facial expression analytics toanalyze images of an individual's face and generates conduct attributes334 describing the individual's emotional state based on theirexpressions. For example, if a person is frowning and their brow isfurrowed, deportment analysis models 326 may infer that the person isangry or annoyed. If the person is pressing their lips together andshuffling their feet, the person may be feeling uncertain or pensive.These emotions are identified in conduct attributes 334. Deportmentanalysis model 326 analyzes body language that is visible in images of aperson's body motions and movements, as well as other attributesindicating movements of the person's feet, hands, posture, feet, legs,and arms to identify conduct attributes describing the person's manner,attitude, and conduct. For example, and without limitation, someonefeeling defensive may cross their arms and lean away from others.Someone that is engaged and interested may lean towards a speaker.Deportment analysis model 326 utilizes vocalization analytics to analyzeset of events 320 and identification attributes 314 to identify soundsmade by the individual and words spoken by the individual. Vocalizationsmay include, words spoken, volume of sounds, and non-verbal sounds.

Comportment analysis model 328 analyzes set of events 320 to identifyconduct attributes 334 indicating an overall level of refinement inmovements and overall smooth conduct and successful completion of taskswithout hesitancy, accident, or mistakes. The term comportment refers tohow refined or unrefined the person's overall manner appears.Comportment analysis model 328 attempts to determine whether the personsoverall behavior is refined, smooth, confident, rough, uncertain,hesitant, unrefined, or how well the person is able to complete tasks.

The term social interactions refers to social manner and the manner inwhich the person interacts with other people and with animals. Socialinteractions analysis model 330 analyzes set of events 320 described inevents metadata to identify conduct attributes indicating types socialinteractions engaged in by the individual and a level of appropriatenessof the social interactions. The type of social interactions comprisesidentifying interactions of an individual as the interactions typical ofa leader, a follower, a loner, an introvert, an extrovert, a charismaticperson, an emotional person, a calm person, a person actingspontaneously, or a person acting according to a plan.

Marketing analysis models 332 analyzes set of events 320 to identifyconduct attributes 334 that are precursors to a purchase of an item orindicators of interest in purchasing an item. For example, conductattributes 334 that may indicate a purchase of an item may include,without limitation, selecting one item that is frequently purchased intandem with another item. For example, if a customer selects a box ofcereal, this conduct is an indicator that the customer may be receptiveto purchasing milk as well. An indicator of interest in purchasing anitem may be conduct suggesting that the customer is looking at aparticular type of item. For example, if a customer is browsing amagazine rack, the conduct of browsing through reading material is anindicator that the customer may be receptive to purchasing magazines,books, or other reading material. If the customer is looking at booksabout barbeque, the customer's conduct indicates receptiveness topurchasing barbeque related items, such as barbeque sauce, grills, andother products associated with barbeque cooking.

Cohort generation engine 316 selects analysis models for set ofreceptivity analysis models 325 based on proposed future change 301, thetype of events in set of events 320, and the type of description dataavailable. For example, if proposed future change 301 is an offer ofassistance carrying baggage to be given to a traveler and set of events320 and identification attributes 314 includes video data of theindividual's face and facial expressions, body movements, posture, armmovements, hand gestures and finger motions, foot movements, or otherbody motions, cohort generation engine 316 may select deportmentanalysis model 326 to analyze set of events 320 to determine if thetraveler will be receptive to assistance.

In another non-limiting example, if proposed future change 301 is anoffer of a discount if a particular product is purchased by a customerand set of events 320 includes radio frequency identification (RFID) tagreader identification the current contents of a customer's shopping cartand video images of the products on the shelf that the customer islooking at and considering purchasing, cohort generation engine 316 mayselect marketing analysis model 332 to process set of events 320.

Cohort generation engine 316 analyzes events metadata 312 describing setof events 320 and identification attributes 314 with any demographicinformation 324, description data 322, and/or user input 326 in theselected set of receptivity analysis models 325 to form receptivitycohort 336. Receptivity cohort 336 includes a set of members and conductattributes 334/=In another embodiment, cohort generation engine 316optionally compares conduct attributes 334 identified by set ofreceptivity analysis models 325 to patterns of conduct 338 to identifyadditional members of receptivity cohort 336. Patterns of conduct 338are known patterns of conduct that indicate a particular demeanor,attitude, emotional state, or manner of a person. Each different type ofconduct by an individual in different environments results in differentsensor data patterns and different attributes. When a match is foundbetween known patterns of conduct 338 and some of conduct attributes334, the matching pattern may be used to identify attributes and conductof the individual. Likewise, cohort generation engine 316 may compareconduct attributes 334 identified by set of receptivity analysis models325 with purchasing patterns 339 to determine whether an individual islikely to be receptive to a marketing message, a sale, an offer topurchase, an offer to sell, a coupon or discount, or other marketing andretail efforts.

In another embodiment, cohort generation engine 316 optionally comparesconduct attributes 334 identified by set of receptivity analysis models325 to patterns of conduct 338 to identify additional members ofreceptivity cohort 336. Patterns of conduct 338 are known patterns ofconduct that indicate a particular demeanor, attitude, emotional state,or manner of a person. Each different type of conduct by an individualin different environments results in different sensor data patterns anddifferent attributes. When a match is found between known patterns ofconduct 338 and some of conduct attributes 334, the matching pattern maybe used to identify attributes and conduct of the individual. Likewise,cohort generation engine 316 may compare conduct attributes 334identified by set of receptivity analysis models 325 with purchasingpatterns 339 to determine whether an individual is likely to bereceptive to a marketing message, a sale, an offer to purchase, an offerto sell, a coupon or discount, or other marketing and retail efforts.

In yet another embodiment, cohort generation engine 316 also retrievesset of cohorts 340. Set of cohorts 340 is a set of one or more cohortsassociated with the individual. Set of cohorts 340 may include an audiocohort, a video cohort, a biometric cohort, a furtive glance cohort, asensor and actuator cohort, specific risk cohort, a general risk cohort,a predilection cohort, and/or an olfactory cohort. Cohort generationengine 316 optionally analyzes cohort data and attributes of cohorts inset of cohorts 340 with set of events 320, description data 322, andidentification attributes 314 in set of receptivity analysis models 325to generate receptivity cohort 336.

In response to new digital sensor data being generated by sensoranalysis engine 304, cohort generation engine 316 analyzes the newdigital sensor data in set of receptivity analysis models 325 togenerate an updated set of events and an updated receptivity cohort.

Analysis server 300 analyzes conduct attributes 334 for receptivitycohort 336 with any available events metadata 312, any availabledescription data 322 and any available demographic information for themembers of receptivity cohort 334 to generate receptivity score 324. Inother words, analysis server 300 may generate receptivity score 342 byanalyzing only conduct attributes 334. However, if events metadata 312,demographic information 324 or description data 322 is available,analysis server 300 may optionally analyze conduct attributes 334 withevents metadata 312, with description data 322 and/or with demographicinformation 324.

Receptivity score 342 indicates a level of receptiveness of each memberof receptivity cohort 336 to proposed future change 301. In oneembodiment, analysis server 300 generates a receptivity score for eachindividual member of receptivity cohort 336. In this example, thereceptivity score for each member indicates that member's level ofreceptiveness to proposed future change 301. In another embodiment,analysis server 300 generates an overall receptivity score for all themembers in receptivity cohort 336. In this example, the overallreceptivity score indicates the level of receptiveness of all members ofreceptivity cohort 336.

Analysis server 300 compares receptivity score 342 with threshold score344 to determine whether the members of receptivity cohort 336 will bereceptive to proposed future change 301. In one embodiment, thresholdscore 344 is an upper threshold. Analysis server 300 identifies the setof members of receptivity cohort 336 as receptive to proposed futurechange 301 in response to a determination that receptivity score 342exceeds the upper threshold. In another embodiment, threshold score 344is a lower threshold. In this embodiment, analysis server 300 identifiesthe set of members of receptivity cohort 336 as receptive to proposedfuture change 301 in response to a determination that receptivity score342 falls below the lower threshold.

In one embodiment, if analysis server 300 identifies receptivity cohort336 as receptive to proposed future change 301, analysis server 300provides output to a user indicating that the members of receptivitycohort 336 are receptive and/or outputs receptivity score 342 indicatingthe level of receptiveness of the members to proposed future change 301.In another non-limiting example, if analysis server 300 identifiesreceptivity cohort 336 as receptive to proposed future change 301,analysis server 300 presents the proposed future change to the set ofmembers of receptivity cohort 336. Proposed future change 301 may bepresented visually on a video display device, presented in an audioformat using a speaker or other sound producing device, presented in ahard copy form, such as on a paper print out, or via any other means ofpresenting output to a user.

In yet another embodiment, if analysis server 300 identifies receptivitycohort 336 as unreceptive to proposed future change 301, analysis server300 provides output to a user indicating that the members of receptivitycohort 336 are unreceptive and/or outputs receptivity score 342indicating the level of un-receptiveness of the members to proposedfuture change 301. In another non-limiting example, if analysis server300 identifies receptivity cohort 336 as unreceptive to proposed futurechange 301, analysis server 300 refrains from presenting the proposedfuture change to the set of members of receptivity cohort 336.

Analysis server 300 continues to analyze new conduct attributes 334 forreceptivity cohort 336 and generates updated receptivity scores forreceptivity cohort 336 as conduct attributes 334 changes and as newconduct attributes are received. In this manner, analysis server 300 cangenerate a series of receptivity scores over a given period of time andalert a user or take an action when the receptivity score indicates thata member of receptivity cohort 336 is sufficiently receptive. In such acase, proposed future change 301 may be presented to one or more membersof receptivity cohort 334 at a later time or in a different locationwhen receptivity score 342 indicates that the member(s) are morereceptive to proposed future change 301.

In another embodiment, analysis server 300 may retrieve granulardemographic information for the set of members of receptivity cohort336. In this example, analysis server 300 processes the granulardemographic information with events metadata 312 and conduct attributes334 to generate a weighted receptivity score for each member ofreceptivity cohort 336. The weighted receptivity score indicates anormalized level of receptivity of the each member to proposed futurechange 301.

In still another embodiment, analysis server 300 assigns a value to eachconduct attribute in the set of conduct attributes. Analysis server 300then aggregates the value of each conduct attribute associated with eachmember of receptivity cohort 336 to generate receptivity score 342.Receptivity score 342 in this non-limiting example is an aggregation ofthe values for the set of members.

In yet another embodiment, analysis server 300 generates receptivityscore 342 by comparing conduct attributes 334 with known patterns ofconduct 338 that are expected to be observed in a person that isreceptive to a proposed future change. In this example, each knownpattern in patterns of conduct 338 is assigned a receptivity value. Thevalues for known patterns that match conduct attributes 334 are weightedand aggregated to generate receptivity score 342.

Referring now to FIG. 4, a block diagram of a set of multimodal sensorsis depicted in accordance with an illustrative embodiment. Set ofmultimodal sensors 400 is a set of sensors that gather sensor dataassociated with a set of individuals. In this non-limiting example, setof multimodal sensors 400 includes set of audio sensors 402, set ofcameras 404, set of biometric sensors 406, set of sensors and actuators408, set of chemical sensors 410, and any other types of devices forgathering data associated with a set of objects and transmitting thatdata to an analysis engine, such as sensor analysis engine 304. Set ofmultimodal sensors 400 detect, capture, and/or record multimodal sensordata 412.

Set of audio sensors 402 is a set of audio input devices that detect,capture, and/or record vibrations, such as, without limitation, pressurewaves, and sound waves. Vibrations may be detected as the vibrations aretransmitted through any medium, such as, a solid object, a liquid, asemisolid, or a gas, such as the air or atmosphere. Set of audio sensors402 may include only a single audio input device, as well as two or moreaudio input devices. An audio sensor in set of audio sensors 402 may beimplemented as any type of device that can detect vibrations transmittedthrough a medium, such as, without limitation, a microphone, a sonardevice, an acoustic identification system, or any other device capableof detecting vibrations transmitted through a medium.

Set of cameras 404 may be implemented as any type of known or availablecamera(s). A cameral may be, without limitation, a video camera forgenerating moving video images, a digital camera capable of taking stillpictures and/or a continuous video stream, a stereo camera, a webcamera, and/or any other imaging device capable of capturing a view ofwhatever appears within the camera's range for remote monitoring,viewing, or recording of an object or area. Various lenses, filters, andother optical devices such as zoom lenses, wide-angle lenses, mirrors,prisms, and the like, may also be used with set of cameras 404 to assistin capturing the desired view. A camera may be fixed in a particularorientation and configuration, or it may, along with any opticaldevices, be programmable in orientation, light sensitivity level, focusor other parameters.

Set of cameras 404 may be implemented as a stationary camera and/ornon-stationary camera. A stationary camera is in a fixed location. Anon-stationary camera may be capable of moving from one location toanother location. Stationary and non-stationary cameras may be capableof tilting up, down, left, and right, panning, and/or rotating about anaxis of rotation to follow or track an object in motion or keep theobject, within a viewing range of the camera lens. The image and/oraudio data in multimodal sensor data 412 that is generated by set ofcameras 404 may be a sound file, a media file, a moving video file, astill picture, a set of still pictures, or any other form of image dataand/or audio data. Video and/or audio data 404 may include, for exampleand without limitation, images of a person's face, an image of a part orportion of a customer's car, an image of a license plate on a car,and/or one or more images showing a person's behavior. In a non-limitingexample, an image showing a customer's behavior or appearance may show acustomer wearing a long coat on a hot day, a customer walking with twotoddlers, a customer moving in a hurried or leisurely manner, or anyother type behavior of one or more objects.

Set of biometric sensors 406 is a set of one or more devices forgathering biometric data associated with a human or an animal. Biometricdata is data describing a physiological state, physical attribute, ormeasurement of a physiological condition. Biometric data may include,without limitation, fingerprints, thumbprints, palm prints, footprints,hear rate, retinal patterns, iris patterns, pupil dilation, bloodpressure, respiratory rate, body temperature, blood sugar levels, andany other physiological data. Set of biometric sensors 406 may include,without limitation, fingerprint scanners, palm scanners, thumb printscanners, retinal scanners, iris scanners, wireless blood pressuremonitor, heart monitor, thermometer or other body temperaturemeasurement device, blood sugar monitor, microphone capable of detectingheart beats and/or breath sounds, a breathalyzer, or any other type ofbiometric device.

Set of sensors and actuators 408 is a set of devices for detecting andreceiving signals from devices transmitting signals associated with theset of objects. Set of sensors and actuators 408 may include, withoutlimitation, radio frequency identification (RFID) tag readers, globalpositioning system (GPS) receivers, identification code readers, networkdevices, and proximity card readers. A network device is a wirelesstransmission device that may include a wireless personal area network(PAN), a wireless network connection, a radio transmitter, a cellulartelephone, Wi-Fi technology, Bluetooth technology, or any other wired orwireless device for transmitting and receiving data. An identificationcode reader may be, without limitation, a bar code reader, a dot codereader, a universal product code (UPC) reader, an optical characterrecognition (OCR) text reader, or any other type of identification codereader. A GPS receiver may be located in an object, such as a car, aportable navigation system, a personal digital assistant (PDA), acellular telephone, or any other type of object.

Set of chemical sensors 410 may be implemented as any type of known oravailable device that can detect airborne chemicals and/or airborne odorcausing elements, molecules, gases, compounds, and/or combinations ofmolecules, elements, gases, and/or compounds in an air sample, such as,without limitation, an airborne chemical sensor, a gas detector, and/oran electronic nose. In one embodiment, set of chemical sensors 410 isimplemented as an array of electronic olfactory sensors and a patternrecognition system that detects and recognizes odors and identifiesolfactory patterns associated with different odor causing particles. Thearray of electronic olfactory sensors may include, without limitation,metal oxide semiconductors (MOS), conducting polymers (CP), quartzcrystal microbalance, surface acoustic wave (SAW), and field effecttransistors (MOSFET). The particles detected by set of chemical sensorsmay include, without limitation, atoms, molecules, elements, gases,compounds, or any type of airborne odor causing matter. Set of chemicalsensors 410 detects the particles in the air sample and generatesolfactory pattern data in multimodal sensor data 412.

Multimodal sensor data 412 may be in an analog format, in a digitalformat, or some of the multimodal sensor data may be in analog formatwhile other multimodal sensor data may be in digital format.

FIG. 5 is a block diagram of a set of cohorts used to generate areceptivity score in accordance with an illustrative embodiment. Set ofcohorts 500 is a set of one or more cohorts associated with a set ofindividuals, such as set of cohorts 340. Set of cohorts 500 may be usedto generate a receptivity cohort. The attributes in set of cohorts 500may also be analyzed with conduct attributes for the receptivity cohortto generate a receptivity score for the receptivity cohort.

General risk cohort 502 is a cohort having members that are general orgeneric rather than specific. Each member of general risk cohort 502comprises data describing objects belonging to a category. A categoryrefers to a class, group, category, or kind A member of a general cohortis a category or sub-cohort including general or average and the risksassociated with those members. Specific risk cohort 504 is a cohorthaving members that are specific, identifiable individuals and the risksassociated with the members of the cohort. Furtive glance cohort 506 isa cohort comprising attributes describing eye movements by members ofthe cohort. The furtive glance attributes describe eye movements, suchas, but without limitation, furtive, rapidly shifting eye movements,rapid blinking, fixed stare, failure to blink, rate of blinking, lengthof a fixed stare, pupil dilations, or other eye movements.

A predilection is the tendency or inclination to take an action orrefrain from taking an action. Predilection cohort 508 comprisesattributes indicating whether an identified person will engage in orperform a particular action given a particular set of circumstances.Audio cohort 510 is a cohort comprising a set of members associated withattributes identifying a sound, a type of sound, a source or origin of asound, identifying an object generating a sound, identifying acombination of sounds, identifying a combination of objects generating asound or a combination of sounds, a volume of a sound, and sound waveproperties.

Olfactory cohort 512 is a cohort comprising a set of members associatedwith attributes a chemical composition of gases and/or compounds in theair sample, a rate of change of the chemical composition of the airsample over time, an origin of gases in the air sample, anidentification of gases in the air sample, an identification of odorcausing compounds in the air sample, an identification of elements orconstituent gases in the air sample, an identification of chemicalproperties and/or chemical reactivity of elements and/or compounds inthe air sample, or any other attributes of particles into the airsample.

Biometric cohort 514 is a set of members that share at least onebiometric attribute in common. A biometric attribute is an attributedescribing a physiologic change or physiologic attribute of a person,such as, without limitation, heart rate, blood pressure, finger print,thumb print, palm print, retinal pattern, iris pattern, blood type,respiratory rate, blood sugar level, body temperature, or any otherbiometric data.

Video cohort 516 is a cohort having a set of members associated withvideo attributes. Video attributes may include, without limitation, adescription of a person's face, color of an object, texture of a surfaceof an object, size, height, weight, volume, shape, length, width, or anyother visible features of the cohort member.

Sensor and actuator cohort 518 includes a set of members associated withattributes describing signals received from sensors or actuators. Anactuator is a device for moving or controlling a mechanism. A sensor isa device that gathers information describing a condition, such as,without limitation, temperature, pressure, speed, position, and/or otherdata. A sensor and/or actuator may include, without limitation, a barcode reader, an electronic product code reader, a radio frequencyidentification (RFID) reader, oxygen sensors, temperature sensors,pressure sensors, a global positioning system (GPS) receiver, alsoreferred to as a global navigation satellite system receiver, Bluetooth,wireless blood pressure monitor, personal digital assistant (PDA), acellular telephone, or any other type of sensor or actuator.

Comportment and deportment cohort 522 is a cohort having membersassociated with attributes identifying a demeanor and manner of themembers, social manner, social interactions, and interpersonal conductof people towards other people and towards animals. Deportment andComportment cohort 522 may include attributes identifying the way aperson behaves toward other people, demeanor, conduct, behavior,manners, social deportment, citizenship, swashbuckling, correctitude,properness, propriety, improperness, impropriety, and personal manner.Swashbuckling refers to flamboyant, reckless, or boastful behavior.Deportment and Comportment cohort 522 may include attributes identifyinghow refined or unrefined the person's overall manner appears.

FIG. 6 is a block diagram of description data for an individual inaccordance with an illustrative embodiment. Description data 600 is datacomprising identification data, past history information, and currentstatus information for an individual, such as description data 322 inFIG. 3. In this example, description data include the individual name,driving history, medical history, educational history, and purchasehistory. For example, and without limitation, purchase history mayinclude brand name products that have been purchased by an individual,the sizes of various products that are typically purchased, the storeswhere the individual shops, the quantities that have been purchased,discounts and coupons that have been used, and other customer purchaseand shopping history information. Current status information is anycurrent information, such as currently scheduled trips, such as a bookedflight to Paris, current status of a driver's license, currentresidence, current income, current credit score, current status on loanpayments or credit card payments, and other current status information.The embodiments are not limited to this description data or this type ofdescription data. The embodiments may be implemented with any type ofpre-generated information describing events associated with theindividual's current status and/or past history.

FIG. 7 is a flowchart of a block diagram of a value assigned to aconduct attribute in accordance with an illustrative embodiment. In onenon-limiting example, an analysis server, such as analysis server 300 inFIG. 3, generates a receptivity score for a cohort by assigning a valueto each conduct attribute from the set of conduct attributes. Conductattribute 700 is an attribute describing a facial expression, bodylanguage, vocalization, social interaction, or other movement or motionby an individual. Conduct attribute 700 is an indicator of thereceptiveness of the individual. The analysis server checks a look-uptable or other database to identify scoring value 702 for each conductattribute. The analysis server then aggregates the values for eachconduct attribute to generate the receptivity score. In this example,but without limitation, the values assigned to each conduct attributeare assigned from a data structure storing at least one of conductattribute values and weighting factors 704.

A weighting factor is a factor or circumstance that results in giving aparticular conduct greater weight or lesser weight due to thatcircumstance. For example, if an adult is yelling or speaking in araised voice, that conduct is a conduct attribute that indicatesreceptivity of the person speaking. Yelling may indicate that a personis angry, distracted, upset or violent. If an adult is yelling at acompanion to get out of the street because a car is coming, theweighting may be lower than if a customer is yelling at a clerk in abank. The circumstance in which the conduct occur influence theweighting. Thus, conduct attribute values may also be weighted based onan identification of the actor, the location of the actor, behavior thatis typical for the actor's demographic group under similarcircumstances, and the actor's own past behavior.

Turning now to FIG. 8, a flowchart of a process for generating areceptivity score is shown in accordance with an illustrativeembodiment. The process in FIG. 8 may be implemented by software forgenerating a receptivity score for a cohort, such as analysis server 300in FIG. 3. The process begins by identifying a receptivity cohort thatincludes a set of members and a set of conduct attributes (step 802).Conduct attributes describe facial expressions, body language,vocalizations, social interactions, and/or any other body movements thatare indicators of receptiveness. The process makes a determination as towhether description data is available (step 804). If description data isavailable, the process retrieves the description data for the set ofmembers (step 806). The process analyzes the set of conduct attributesand events metadata with any available description data to generate areceptivity score (step 808). The receptivity score indicates a level ofreceptiveness of the set of members to a proposed future change incircumstances associated with at least one member in the set of members.The process makes a determination as to whether the receptivity scoreexceeds an upper threshold or falls below a lower threshold (step 810).If the receptivity score does exceed an upper threshold or falls below alower threshold, the process identifies the set of member of thereceptivity cohort as receptive to the proposed future change (step 812)with the process terminating thereafter.

Returning to step 810, if the receptivity score does not exceed theupper threshold or fall the lower threshold, the process identifies theset of members of the receptivity cohort as unreceptive to the proposedfuture change (step 814) with the process terminating thereafter.

In this embodiment, the threshold is both an upper threshold and a lowerthreshold. In another embodiment, only a lower threshold is used forcomparison with the receptivity score. In another example, only an upperthreshold is used for comparison with the receptivity score. In yetanother non-limiting example, a series of thresholds is used forcomparison with the receptivity score. For example, the initialreceptivity score may be compared to a first threshold. In response toreceiving new events metadata and/or new conduct attributes, a secondreceptivity score may be generated. The second receptivity score maythen be compared to a second threshold. In response to new eventsmetadata and/or new conduct attributes, a third receptivity score may begenerated that is compared to a third threshold, and so forthiteratively for as long as new input data is available. As shown here,each threshold may be only an upper threshold, only a lower threshold,or both an upper threshold and a lower threshold.

Thus, according to one embodiment of the present invention, a computerimplemented method, apparatus, and computer program product forgenerating receptivity scores for cohorts is provided. A receptivitycohort is identified. The receptivity cohort includes a set of membersand conduct attributes for the set of members. Each conduct attribute inthe set of conduct attributes describes at least one of a facialexpression, vocalization, body language, and social interactions of amember in the set of members. Each conduct attribute is an indicator ofreceptiveness to a proposed future change in a set of circumstancesassociated with the set of members. Events metadata is received. Theevents metadata describes the set of circumstances associated with theset of members. The set of conduct attributes and the events metadata isanalyzed to generate a receptivity score for the receptivity cohort. Thereceptivity score indicates a level of receptiveness of the set ofmembers to the proposed future change in the set of circumstances. Theset of members of the receptivity cohort are identified as receptive tothe proposed future change based on the result of a comparison of thereceptivity score to a threshold score.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer-usable or computer readable medium can be any tangibleapparatus that can store the program for use by or in connection withthe instruction execution system, apparatus, or device.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A processor-implemented method of generating arecommendation to add a member to a receptivity cohort, theprocessor-implemented method comprising: defining a receptivity cohort,wherein each member of the receptivity cohort shares a conductattribute, wherein the conduct attribute describes at least one of afacial expression, body language, and social interaction of a person,and wherein the conduct attribute has been predetermined to be anindicator of a level of receptiveness to a proposed future change in aset of circumstances; a processor analyzing the conduct attribute andevent metadata to generate a receptivity score for the receptivitycohort, wherein event metadata describes the proposed future change inthe set of circumstances, and wherein the receptivity score indicates alevel of receptiveness of members of the receptivity cohort to theproposed future change in the set of circumstances; in response todetermining that granular demographic information for members of thereceptivity cohort is available, the processor processing the granulardemographic information with the event metadata and conduct attribute togenerate a weighted receptivity score for each member of the receptivitycohort, wherein the weighted receptivity score indicates a normalizedlevel of receptivity of said each member to the proposed future change;the processor retrieving biometric sensor data from a set of biometricsensors, wherein the biometric sensor data describe said at least one ofthe facial expression, body language and social interaction for acandidate member of the receptivity cohort; the processor comparing thebiometric sensor data for the candidate member to said conduct attributeof members of the receptivity cohort; and the processor, in response tothe biometric sensor data for the candidate member matching the conductattribute of members of the receptivity cohort, generating arecommendation to add the candidate member to the receptivity cohort. 2.The processor-implemented method of claim 1, wherein the biometricsensor data from the set of biometric sensors is retrieved in responseto receiving a notification of the proposed future change.
 3. Theprocessor-implemented method of claim 1, wherein the receptivity scorecomprises an upper threshold, and wherein members of the receptivitycohort are identified as receptive to the proposed future change inresponse to a determination that the receptivity score exceeds the upperthreshold.
 4. The processor-implemented method of claim 1, wherein thereceptivity score comprises a lower threshold, and wherein members ofthe receptivity cohort are identified as receptive to the proposedfuture change in response to a determination that the receptivity scorefalls below the lower threshold.
 5. The processor-implemented method ofclaim 1, wherein the conduct attribute is from a set of conductattributes, and wherein the processor-implemented method furthercomprises: assigning a value to each conduct attribute in the set ofconduct attributes; and aggregating the value of said each conductattribute associated with each member of the receptivity cohort togenerate a receptivity score, wherein the receptivity score is anaggregation of receptivity values for all members of the receptivitycohort.
 6. The processor-implemented method of claim 1, wherein theconduct attribute is from a set of conduct attributes, and wherein theprocessor-implemented method further comprises: comparing the set ofconduct attributes with known patterns of expected conduct associatedwith a person that is receptive to the proposed future change togenerate a receptivity score, wherein each known pattern is assigned areceptivity value, and wherein values for known patterns that matchconduct attributes for the receptivity cohort are weighted andaggregated to generate the receptivity score.
 7. Theprocessor-implemented method of claim 1, wherein event metadatadescribes the proposed future change in the set of circumstances, andwherein the processor-implemented method further comprises: responsiveto determining description data for each member of the receptivitycohort is available, retrieving the description data for each member ofthe receptivity cohort, wherein the description data comprises at leastone of identification information, past history information, and currentstatus information for said each member of the receptivity cohort; andanalyzing the description data with the conduct attribute and the eventmetadata to generate a receptivity score for said each member of thereceptivity cohort.
 8. The processor-implemented method of claim 1,wherein the proposed future change in the set of circumstances is atleast one of a proposed future change in work location requiring that aworker relocate or commute across a greater distance, a proposed offerto sell goods or services to a customer at a given price, an offer topurchase goods or services from a person, a proposed request that aperson leave a particular location, and a request that a person stopperforming a given action.
 9. A computer program product for generatinga recommendation to add a member to a receptivity cohort, the computerprogram product comprising: a non-transitory computer readable storagemedium; first program instructions to define a receptivity cohort,wherein each member of the receptivity cohort shares a conductattribute, wherein the conduct attribute is from a set of conductattributes, wherein the conduct attribute describes at least one of afacial expression, body language, and social interaction of a person,and wherein the conduct attribute has been predetermined to be anindicator of a level of receptiveness to a proposed future change in aset of circumstances; second program instructions to analyze the conductattribute and event metadata to generate a receptivity score for thereceptivity cohort, wherein event metadata describes the proposed futurechange in the set of circumstances, and wherein the receptivity scoreindicates a level of receptiveness of members of the receptivity cohortto the proposed future change in the set of circumstances; third programinstructions to, in response to determining that granular demographicinformation for members of the receptivity cohort is available, processthe granular demographic information with the event metadata and conductattribute to generate a weighted receptivity score for each member ofthe receptivity cohort, wherein the weighted receptivity score indicatesa normalized level of receptivity of said each member to the proposedfuture change; fourth program instructions to retrieve biometric sensordata from a set of biometric sensors, wherein the biometric sensor datadescribe said at least one of the facial expression, body language andsocial interaction for a candidate member of the receptivity cohort;fifth program instructions to compare the biometric sensor data for thecandidate member to said conduct attribute of members of the receptivitycohort; and sixth program instructions to, in response to the biometricsensor data for the candidate member matching the conduct attribute ofmembers of the receptivity cohort, generate a recommendation to add thecandidate member to the receptivity cohort; and wherein the first,second, third, fourth, fifth, and sixth program instructions are storedon the non-transitory computer readable storage medium.
 10. The computerprogram product of claim 9, wherein the biometric sensor data from theset of biometric sensors is retrieved in response to receiving anotification of the proposed future change.
 11. The computer programproduct of claim 9, wherein the receptivity score comprises an upperthreshold, and wherein members of the receptivity cohort are identifiedas receptive to the proposed future change in response to adetermination that the receptivity score exceeds the upper threshold.12. The computer program product of claim 9, wherein the receptivityscore comprises a lower threshold, and wherein members of thereceptivity cohort are identified as receptive to the proposed futurechange in response to a determination that the receptivity score fallsbelow the lower threshold.
 13. A computer system comprising: aprocessor, a computer readable memory, and a computer readable storagemedia; first program instructions to define a receptivity cohort,wherein each member of the receptivity cohort shares a conductattribute, wherein the conduct attribute is from a set of conductattributes, wherein the conduct attribute describes at least one of afacial expression, body language, and social interaction of a person,and wherein the conduct attribute has been predetermined to be anindicator of a level of receptiveness to a proposed future change in aset of circumstances; second program instructions to analyze the conductattribute and event metadata to generate a receptivity score for thereceptivity cohort, wherein event metadata describes the proposed futurechange in the set of circumstances, and wherein the receptivity scoreindicates a level of receptiveness of members of the receptivity cohortto the proposed future change in the set of circumstances; third programinstructions to, in response to determining that granular demographicinformation for members of the receptivity cohort is available, processthe granular demographic information with the event metadata and conductattribute to generate a weighted receptivity score for each member ofthe receptivity cohort, wherein the weighted receptivity score indicatesa normalized level of receptivity of said each member to the proposedfuture change; fourth program instructions to retrieve biometric sensordata from a set of biometric sensors, wherein the biometric sensor datadescribe said at least one of the facial expression, body language andsocial interaction for a candidate member of the receptivity cohort;fifth program instructions to compare the biometric sensor data for thecandidate member to said conduct attribute of members of the receptivitycohort; and sixth program instructions to, in response to the biometricsensor data for the candidate member matching the conduct attribute ofmembers of the receptivity cohort, generate a recommendation to add thecandidate member to the receptivity cohort; and wherein the first,second, third, fourth, fifth, and sixth program instructions are storedon the computer readable storage media for execution by the processorvia the computer readable memory.
 14. The computer system of claim 13,wherein the biometric sensor data from the set of biometric sensors isretrieved in response to receiving a notification of the proposed futurechange.
 15. The computer system of claim 13, wherein event metadatadescribes the proposed future change in the set of circumstances, andwherein the computer system further comprises: seventh programinstructions to analyze the conduct attribute and event metadata togenerate the receptivity score for the receptivity cohort, wherein thereceptivity score indicates a level of receptiveness of members of thereceptivity cohort to the proposed future change in the set ofcircumstances; and wherein the seventh program instructions are storedon the computer readable storage media for execution by the processorvia the computer readable memory.
 16. The computer system of claim 13,wherein the receptivity score comprises an upper threshold, and whereinmembers of the receptivity cohort are identified as receptive to theproposed future change in response to a determination that thereceptivity score exceeds the upper threshold.
 17. The computer systemof claim 13, wherein the receptivity score comprises a lower threshold,and wherein members of the receptivity cohort are identified asreceptive to the proposed future change in response to a determinationthat the receptivity score falls below the lower threshold.